Information on human settlements is crucial for a wide range of applications including emergency response, disaster risk reduction, population estimation/analysis, and urban/regional planning Urbanization pressure generates environmental impacts, indicates population growth, and relates to risk and disaster vulnerability. For instance, the global population passed the mark of 7.0 billion in 2011 with more than half of the population living in urban areas. Between 2011 and 2050, the urban population is expected to increase by about 2.7 billion, passing from 3.6 billion in 2011 to 6.3 billion in 2050. The population growth in urban areas is projected to be concentrated in the cities and towns of the less developed countries and continents. Asia, in particular, is projected to see its urban population increase by 1.4 billion, Africa by 0.9 billion, and Latin America and the Caribbean by 0.2 billion.
Population growth is therefore becoming largely an urban phenomenon concentrated in the developing world resulting in major challenges to manage the urban development in a sustainable manner. A central issue in this respect is the availability of up-to-date information on the extent and quality of the urban settlement (e.g., the urban “build-up” or “built-up”) which is largely unavailable in developing countries. For instance, cities are often growing at a pace that cannot be fully controlled by the local or regional mapping agencies. As demographic pressure increases exponentially at a global level, the ability to monitor, quantify and characterize urbanization processes around the world is becoming paramount. The information about the quality of urban development can provide precious input for understanding the vulnerability of the population living on our planet.
While satellite imagery could provide information about the world-wide built-up environment, there are few global data sets available that could be used to map the human settlements. Examples include the night-time lights of the world based on the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) sensor, Moderate Resolution Imaging Spectroradiometer (MODIS) based land use/land cover classifications, and global population data sets like LandScan™ or the gridded population of the world (GPW). While the aforementioned data sets are useful for global analysis, the data sets have the tendency to under-represent small, scattered rural settlements due to the low spatial resolution of the data sets between, for instance, 500 and 2,000 m. Furthermore, the data sets represent single snap-shots in time that do not allow for regular monitoring. Still further, if the data sets are updated (e.g., the LandScan data set), they are not directly comparable due to changing input sources.